Comments (2)
Hi @DimanChuancey sorry about the delay.
Rank is the rank of the decomposition so you can set it as you like.
In general lower than the shape (low-rank weighths), you could have a rank higher than the shape (over-parametrized). You can have a look at the doc. If you want a good intro to tensor decomposition I highly recommend the great survey by Kolda and Bader, “Tensor Decompositions and Applications”, SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
from tensorly-notebooks.
Hi @DimanChuancey sorry about the delay.
Rank is the rank of the decomposition so you can set it as you like.
In general lower than the shape (low-rank weighths), you could have a rank higher than the shape (over-parametrized). You can have a look at the doc. If you want a good intro to tensor decomposition I highly recommend the great survey by Kolda and Bader, “Tensor Decompositions and Applications”, SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
Thanks! Good wishes!
from tensorly-notebooks.
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